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Title: :  CHEST X-RAY BASED DISEASE ANALYSIS USING CNN
PaperId: :  22773
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22773
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Talatam Aadi Narayana MurthyVasireddy Venkatadri Institute of Technology
Somabathina AravindVasireddy Venkatadri Institute of Technology
Ravuri Anil KumarVasireddy Venkatadri Institute of Technology
Shaik Mohammed KhalidVasireddy Venkatadri Institute of Technology

Abstract

Machine Learning
X-ray images, Deep Learning, AUC-ROC, Accuracy, Pneumonia
The paper is centered around advancing medical diagnostics through the utilization of chest X-ray scans for the detection of respiratory and cardiac conditions. The primary focus is on developing and implementing a machine learning-based system for automated disease detection in chest X-ray images. The principal objective of this paper is to develop a resilient and precise system that can autonomously detect and categorize anomalies, specifically pneumonia, in chest X-ray scans. This is obtained and accomplished through the application of machine learning algorithms, including sophisticated techniques like Convolutional Neural Networks (CNNs). These algorithms analyze radiological images to recognize and categorize pathological conditions, contributing to more efficient and precise medical diagnostics. Key components of the paper include data collection, model development, and performance evaluation. In the data collection phase, a diverse dataset of chest X-ray images is compiled, ensuring comprehensive coverage of labeled disease conditions to encompass a wide spectrum of cases. The model development phase explores various machine learning architectures to construct an accurate disease detection model, with an emphasis on leveraging CNNs for their efficacy in image analysis. Performance evaluation is conducted using standard medical imaging metrics, including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC). These metrics provide a comprehensive assessment of the system's ability to correctly identify and classify diseases in chest X-ray scans. In addition to the technical aspects, the paper aims to enhance user interaction by creating a user-friendly interface tailored for radiologists and healthcare practitioners. This interface facilitates automated preliminary analysis, expediting the diagnostic process and supporting healthcare professionals in making quicker and more informed decisions. By integrating cutting-edge machine learning techniques, robust data collection, and user-friendly interface design, this paper strives to contribute significantly to the field of medical diagnosis, particularly in the automated detection of respiratory and cardiac conditions from chest X-ray images.

Citations

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IJARIIE Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. "CHEST X-RAY BASED DISEASE ANALYSIS USING CNN" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 513-526
MLA Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. "CHEST X-RAY BASED DISEASE ANALYSIS USING CNN." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 513-526.
APA Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, & Shaik Mohammed Khalid. (2024). CHEST X-RAY BASED DISEASE ANALYSIS USING CNN. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 513-526.
Chicago Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. "CHEST X-RAY BASED DISEASE ANALYSIS USING CNN." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 513-526.
Oxford Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. 'CHEST X-RAY BASED DISEASE ANALYSIS USING CNN', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 513-526. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/CHEST_X_RAY_BASED_DISEASE_ANALYSIS_USING_CNN_ijariie22773.pdf (Accessed : 17 March 2024).
Harvard Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. (2024) 'CHEST X-RAY BASED DISEASE ANALYSIS USING CNN', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 513-526IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/CHEST_X_RAY_BASED_DISEASE_ANALYSIS_USING_CNN_ijariie22773.pdf (Accessed : 17 March 2024)
IEEE Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid, "CHEST X-RAY BASED DISEASE ANALYSIS USING CNN," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 513-526, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/CHEST_X_RAY_BASED_DISEASE_ANALYSIS_USING_CNN_ijariie22773.pdf [Accessed : 17 March 2024].
Turabian Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. "CHEST X-RAY BASED DISEASE ANALYSIS USING CNN." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (17 March 2024).
Vancouver Talatam Aadi Narayana Murthy, Somabathina Aravind, Ravuri Anil Kumar, and Shaik Mohammed Khalid. CHEST X-RAY BASED DISEASE ANALYSIS USING CNN. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 17 March 2024]; 10(2) : 513-526. Available from: https://ijariie.com/AdminUploadPdf/CHEST_X_RAY_BASED_DISEASE_ANALYSIS_USING_CNN_ijariie22773.pdf
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